Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

In recent years, the exponential growth of digital documents has been met by rapid progress
in text classification techniques. Newly proposed machine learning algorithms leverage the …

Large language models (LLMs) have been shown to be able to perform new tasks based on
a few demonstrations or natural language instructions. While these capabilities have led to …

Recent language models have shown impressive multilingual performance, even when not
explicitly trained for it. Despite this, there are concerns about the quality of their outputs …

Current image generation models struggle to reliably produce well-formed visual text. In this
paper, we investigate a key contributing factor: popular text-to-image models lack character …

Multimodal models are becoming increasingly effective, in part due to unified components,
such as the Transformer architecture. However, multimodal models still often consist of many …

Deep neural-network-based language models (LMs) are increasingly applied to large-scale
protein sequence data to predict protein function. However, being largely black-box models …

This paper introduces mGPT, a multilingual variant of GPT-3, pretrained on 61 languages
from 25 linguistically diverse language families using Wikipedia and the C4 Corpus. We …

The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to
decompose a word into a sequence of morphemes and covered most types of morphology …

Text-editing models have recently become a prominent alternative to seq2seq models for
monolingual text-generation tasks such as grammatical error correction, simplification, and …